PixelCNN
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 16 June 2016
- Authors
- Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image generation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 15,728,640,000 tokens
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 32
- Wall-clock time
- 60 hours
We were able to achieve similar performance to the PixelRNN (Row LSTM [30]) in less than half the training time (60 hours using 32 GPUs).
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited,SOTA improvement
- Record confidence
- Unknown
- Citations
- 3,079
Best performance on NLL test.
Sources
Where this record came from and when it was last checked.
- Reference
- Conditional Image Generation with PixelCNN Decoders
- Last updated
- 28 November 2025
What the numbers mean
About this model
PixelCNN was published by Google DeepMind, in United States of America, in June 2016. The organisation is categorised as industry.
It works in Vision, and is recorded as doing image generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Around 15,728,640,000 tokens went into training it.
The reason it appears in this catalogue at all is highly cited,SOTA improvement.
Answers
PixelCNN — common questions
What GPU do I need to run PixelCNN?
None. PixelCNN is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is PixelCNN open source?
The licensing for PixelCNN was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does PixelCNN have?
No parameter count has been published for PixelCNN, which is why no memory or speed figure appears on this page.
Who created PixelCNN?
PixelCNN was published by Google DeepMind, based in United States of America, categorised as industry.
When was PixelCNN released?
PixelCNN was published in June 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is PixelCNN used for?
PixelCNN works in Vision, and is recorded as handling image generation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.